| 书目名称 | Turbo Message Passing Algorithms for Structured Signal Recovery | | 编辑 | Xiaojun Yuan,Zhipeng Xue | | 视频video | | | 概述 | Provides an in depth look into turbo message passing algorithms for structured signal recovery.Includes efficient iterative algorithmic solutions for inference, optimization, and satisfaction problems | | 丛书名称 | SpringerBriefs in Computer Science | | 图书封面 |  | | 描述 | .This book takes a comprehensive study on turbo message passing algorithms for structured signal recovery, where the considered structured signals include 1) a sparse vector/matrix (which corresponds to the compressed sensing (CS) problem), 2) a low-rank matrix (which corresponds to the affine rank minimization (ARM) problem), 3) a mixture of a sparse matrix and a low-rank matrix (which corresponds to the robust principal component analysis (RPCA) problem). The book is divided into three parts. First, the authors introduce a turbo message passing algorithm termed denoising-based Turbo-CS (D-Turbo-CS). Second, the authors introduce a turbo message passing (TMP) algorithm for solving the ARM problem. Third, the authors introduce a TMP algorithm for solving the RPCA problem which aims to recover a low-rank matrix and a sparse matrix from their compressed mixture. With this book, we wish to spur new researches on applying message passing to various inference problems. .Provides an in depth look into turbo message passing algorithms for structured signal recovery.Includes efficient iterative algorithmic solutions for inference, optimization, and satisfaction problems through message pas | | 出版日期 | Book 2020 | | 关键词 | Message passing; turbo principle; compressed sensing; low-rank; robust principal component analysis; affi | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-54762-2 | | isbn_softcover | 978-3-030-54761-5 | | isbn_ebook | 978-3-030-54762-2Series ISSN 2191-5768 Series E-ISSN 2191-5776 | | issn_series | 2191-5768 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 18:53:29
